{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div align='center'>第2章 参数估计</div>\n",
    "### 内容<br>\n",
    " <div align='left'>                  \n",
    "     <font color='steelblue' size=4>\n",
    "       2.1 点估计<br><br>\n",
    "       2.2 区间估计<br><br>\n",
    "       </font>\n",
    "       </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 点估计-极大似然法\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.1 极大似然法的概念\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  \n",
    "  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.2 连续函数空间的解析解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "前10个值： [  5.19843205  -1.68282638  12.99181887 -11.92810992 -20.11367779\n",
      "  17.07255856   0.1609451   -1.39443596  -7.22645477   9.7967091 ]\n",
      "\n",
      "使用极大似然法估计得到的均值mu：2.45，标准差sigma为：10.14。\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "from  collections import namedtuple \n",
    "#均值为2，标准差为10的正态分布随机变量1000个\n",
    "x = st.norm.rvs(loc=2,scale=10,size=1000)\n",
    "print('前10个值：',x[0:10])\n",
    "\n",
    "#通过上述对数似然方程的估计公式，计算均值和标准差\n",
    "mu = np.sum(x)/len(x)\n",
    "sigma = np.sqrt(np.sum((x-np.mean(x))**2)/len(x))\n",
    "print('\\n使用极大似然法估计得到的均值mu：%0.2f，标准差sigma为：%0.2f。'%(mu,sigma))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1.3 对数似然方程的数值解\n",
    " \n",
    "以柯西(Couchy)分布为例,该总体分布的概率密度函数的尺度参数为$\\gamma$，位置参数$\\theta$。当尺度参数为1，位置参数为0时，称之为柯西分布的标准化形式或标准柯西分布。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fsolve函数求解： [14.77620176]\n",
      "root函数求解 [14.77620176]\n",
      "模拟数据的中位数： 14.700203658768027\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from scipy.optimize import fsolve,root\n",
    "#生成模拟数据\n",
    "data = st.cauchy.rvs(loc=15,scale=1,size = 100)\n",
    "#对数似然方程\n",
    "def func(theta,x):\n",
    "    return np.sum((x-theta)/(1+(x-theta)**2))\n",
    "result = fsolve(func,np.median(data),args=data)\n",
    "print('fsolve函数求解：',result)\n",
    "result1 = root(func,np.median(data),args=data)\n",
    "print('root函数求解',result1.x)\n",
    "print('模拟数据的中位数：',np.median(data))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 牛顿法\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.776896057177604\n",
      "14.776201803565757\n",
      "14.776201763287403\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def cauchy_mle(x,theta):\n",
    "    sum = 0\n",
    "    for i in range(0,len(x)):\n",
    "        sum = sum+(x[i]-theta)/(1+(x[i]-theta)**2)\n",
    "    return sum\n",
    "\n",
    "#对数似然函数的一阶导数\n",
    "def cauchy_mle_der(x,theta):\n",
    "    sum = 0  \n",
    "    for i in range(0,len(x)):\n",
    "        tmp = (x[i]-theta)**2\n",
    "        sum = sum+(tmp-1)/(1+tmp)**2\n",
    "    return sum\n",
    "\n",
    "#用样本中位数作为初始估计值\n",
    "theta=np.median(data)\n",
    "\n",
    "#迭代次数控制，当前一次迭代值与当前迭代值小于0.001时停止迭代。\n",
    "#说明已经收敛。\n",
    "std_diff=1e-18\n",
    "\n",
    "while True:##无限循环\n",
    "    ###牛顿法的迭代公式\n",
    "    theta1 = theta - cauchy_mle(data,theta)/cauchy_mle_der(data,theta)\n",
    "    ###当本次迭代的数值解和上次迭代数值解之差的绝对值小于阈值时，停止迭代，\n",
    "    if np.abs(theta1-theta)<=std_diff:\n",
    "        break\n",
    "    else:\n",
    "        theta=theta1\n",
    "    print(theta)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 区间估计\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 单个正态总体的均值$\\mu$的区间估计\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#命名元组，用来保存函数返回结果\n",
    "MuResultSet = namedtuple('MuResultSet',['Mean','DF','Lower','Upper'])\n",
    "def interval_mu(x, sigma=-1,alpha=0.05):\n",
    "    n = len(x)#样本量\n",
    "    m = np.mean(x)#样本均值\n",
    "    \n",
    "    #根据方差已知与否计算上下限的加减量\n",
    "    if sigma >= 0:#总体方差已知\n",
    "        tmp = (sigma/np.sqrt(n))*st.norm.ppf(1-alpha/2)\n",
    "        df = n\n",
    "    else:#总体方差未知\n",
    "        tmp = (st.tstd(x)/np.sqrt(n))*st.t.ppf(1-alpha/2,n-1)\n",
    "        df = n-1\n",
    "    lower = m - tmp#置信下限\n",
    "    upper = m + tmp#置信上限\n",
    "    \n",
    "    result = MuResultSet(Mean=np.round(m,2),DF=df,\n",
    "                         Lower=np.round(lower,6),\n",
    "                         Upper=np.round(upper,6))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MuResultSet(Mean=14.95, DF=6, Lower=14.78997, Upper=15.11003)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "示例：某机器零件的长度服从N(mu,0.04)分布，随机抽取样本长度如下(单位：mm)：\n",
    "    14.6,15.1,14.9,14.8,15.2,15.1\n",
    "    求零件长度的置信区间0.95的区间估计。\n",
    "'''\n",
    "lengths = np.array([14.6,15.1,14.9,14.8,15.2,15.1])\n",
    "interval_mu(lengths,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MuResultSet(Mean=10.05, DF=9, Lower=9.877225, Upper=10.222775)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "示例：抽样某零件样品的重量共10次，(单位：kg)，分别为：\n",
    "    10.1,10,9.8,10.5,9.7,10.1,9.9,10.2,10.3,9.9\n",
    "    求零件长度的置信区间0.95的区间估计。\n",
    "'''\n",
    "weights = np.array([10.1,10,9.8,10.5,9.7,10.1,9.9,10.2,10.3,9.9])\n",
    "interval_mu(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "通过Scipy的interval函数计算置信区间（方差已知）： [14.78997 15.11003]\n",
      "通过Scipy的interval函数计算置信区间（方差未知）： [ 9.877225 10.222775]\n"
     ]
    }
   ],
   "source": [
    "#零件长度的置信区间，方差已知，使用正态分布的interval函数\n",
    "#此处方差已知，所以直接使用0.2,即方差0.04的平方根\n",
    "interv1 = st.norm.interval(0.95,loc = np.mean(lengths),scale = 0.2/np.sqrt(6))\n",
    "print('通过Scipy的interval函数计算置信区间（方差已知）：',np.round(interv1,6))\n",
    "\n",
    "#零件重量的置信区间，方差未知，使用t分布的interval函数\n",
    "#此处第4个参数同样使用标准误，可以调用scipy的tsem函数计算样本数据的标准误\n",
    "interv2 = st.t.interval(0.95,df = len(weights)-1,loc=np.mean(weights),\n",
    "                        scale=st.tsem(weights))\n",
    "print('通过Scipy的interval函数计算置信区间（方差未知）：',np.round(interv2,6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 单个正态总体的方差$\\sigma^2$的区间估计\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "单个正态总体的方差区间估计，分两种情况：均值已知和均值未知\n",
    "可以用来测量数据的误差大小。\n",
    "'''\n",
    "from  collections import namedtuple \n",
    "#命名元组，用来保存函数返回结果\n",
    "VarResultSet = namedtuple('VarResultSet',['Var','DF','Lower','Upper'])\n",
    "def interval_var(x, mu=float('Inf'),alpha=0.05):\n",
    "    n = len(x)#样本量\n",
    "    \n",
    "    if mu < float('Inf'):#均值已知\n",
    "        S2 = np.sum((x-mu)**2)/n\n",
    "        df = n\n",
    "    else:#均值未知\n",
    "        S2 = st.tvar(x)#使用方差的无偏估计，与np.var区分开。\n",
    "        df = n-1\n",
    "    \n",
    "    lower = df*S2/st.chi2.ppf(1-alpha/2,df)\n",
    "    upper = df*S2/st.chi2.ppf(alpha/2,df)\n",
    "    \n",
    "    result = VarResultSet(Var=S2,DF=df,Lower=np.round(lower,6),Upper=np.round(upper,6))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已知均值为10的方差置信区间: VarResultSet(Var=0.05499999999999999, DF=10, Lower=0.026851, Upper=0.169389)\n",
      "未知均值的方差置信区间: VarResultSet(Var=0.05833333333333332, DF=9, Lower=0.027599, Upper=0.194416)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "示例：抽样某零件样品的重量共10次，(单位：kg)，分别为：\n",
    "    10.1,10,9.8,10.5,9.7,10.1,9.9,10.2,10.3,9.9\n",
    "    求零件长度的测量误差，分为已知均值为10和未知均值两种情况。\n",
    "    结果显示，在均值已知的情况下，计算结果更好，即置信区间更窄。\n",
    "'''\n",
    "weights = np.array([10.1,10,9.8,10.5,9.7,10.1,9.9,10.2,10.3,9.9])\n",
    "#已知均值为10\n",
    "print('已知均值为10的方差置信区间:',interval_var(weights,10))\n",
    "#均值未知\n",
    "print('未知均值的方差置信区间:',interval_var(weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3 两个正态总体均值 $\\mu_1$-$\\mu_2$ 的区间估计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "在实际工作中，经常需要比较两个数据之间的平均值是否存在差异，\n",
    "比如在教学科研中，需比较新老两种教学方法的效果是否存在差异，\n",
    "那么就可以对两种方法的学生成绩的均值进行比较，如果存在显著差异即是效果，\n",
    "反之则无差别。\n",
    "'''\n",
    "def interval_mu2(x,y,sigma=np.array([-1,-1]),vareq=False,alpha=0.05):\n",
    "    n1 = len(x)\n",
    "    n2 = len(y)\n",
    "    mx = np.mean(x)\n",
    "    my = np.mean(y)\n",
    "    \n",
    "    if np.all(sigma>0):#两个独立样本的方差已知\n",
    "        #根据上述公式计算置信区间上、下限的加、减量\n",
    "        tmp = st.norm.ppf(1-alpha/2)*np.sqrt(sigma[0]**2/n1+sigma[1]**2/n2)\n",
    "        df = n1+n2\n",
    "    else:\n",
    "        if vareq:#两个独立样本的方差相等且未知\n",
    "            sw = ((n1-1)*st.tvar(x)+(n2-1)*st.tvar(y))/(n1+n2-2)\n",
    "            tmp = np.sqrt(sw*(1/n1+1/n2))*st.t.ppf(1-alpha/2,n1+n2-2)\n",
    "            df = n1+n2-2\n",
    "        else:#两个独立样本的方差不相等且未知\n",
    "            s1 = st.tvar(x)\n",
    "            s2 = st.tvar(y)\n",
    "            nu = ((s1/n1+s2/n2)**2)/(s1**2/(n1**2*(n1-1))+s2**2/(n2**2*(n2-1)))\n",
    "            tmp = st.t.ppf(1-alpha/2,nu)*np.sqrt(s1/n1+s2/n2)\n",
    "            df = nu\n",
    "    result = MuResultSet(Mean=mx-my,DF=df,\n",
    "                         Lower=np.round(mx-my-tmp,6),\n",
    "                         Upper=np.round(mx-my+tmp,6))\n",
    "    return result"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 示例：\n",
    "![17.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A14_1.jpg](attachment:17.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A14_1.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mu1-mu2置信水平0.95的区间估计为： MuResultSet(Mean=-0.7545086097395775, DF=200, Lower=-1.60636, Upper=0.097343)\n",
      "调用Scipy的t检验函数： Ttest_indResult(statistic=-2.6567913400896224, pvalue=0.008604393572159667)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "示例：比较棉花品种优劣，方差已知。\n",
    "'''\n",
    "#根据均值和标准差生成两个独立样本数据\n",
    "x = st.norm.rvs(loc=5.32,scale=2.18,size=100)\n",
    "y = st.norm.rvs(loc=5.76,scale=1.76,size=100)\n",
    "\n",
    "#调用区间估计函数，方差已知\n",
    "result = interval_mu2(x,y,np.array([2.18,3.76])) \n",
    "print('mu1-mu2置信水平0.95的区间估计为：',result)\n",
    "\n",
    "#调用t检验函数，原假设是两个独立样本具有相同均值\n",
    "result1 = st.ttest_ind(x,y,equal_var=False)\n",
    "print('调用Scipy的t检验函数：',result1)"
   ]
  },
  {
   "attachments": {
    "17.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A14_2.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 示例：\n",
    "![17.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A14_2.jpg](attachment:17.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A14_2.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "方差未知且相同： MuResultSet(Mean=1.0349320762243224, DF=27, Lower=-2.335798, Upper=4.405662)\n",
      "方差未知且不同: MuResultSet(Mean=1.0349320762243224, DF=22.64783987599828, Lower=-1.959092, Upper=4.028956)\n",
      "t检验，方差相同： Ttest_indResult(statistic=0.6299838265950031, pvalue=0.534000814127331)\n",
      "t检验，方差不同： Ttest_indResult(statistic=0.7156802323794383, pvalue=0.4815030308455729)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "示例：比较矿泉水体积，方差未知，相同和不相同两种情况。\n",
    "下述程序运行结果表明，无论何种检验方式，都不能拒绝两条流水线上生辰的矿泉水体积相等。\n",
    "区间估计表明上限大于零，下限小于零，即区间包含了零。\n",
    "t检验的p值大于0.05，不能拒绝两个独立样本的均值相等的原假设。\n",
    "在假设方差不同的情况下，计算结果更加精确，即置信区间更窄。\n",
    "'''\n",
    "x = st.norm.rvs(501.1,2.4,12)\n",
    "y = st.norm.rvs(499.7,4.7,17)\n",
    "#方差未知，且相同\n",
    "result1 = interval_mu2(x,y,vareq=True)\n",
    "print('方差未知且相同：',result1)\n",
    "#方差未知，且不同\n",
    "result2 = interval_mu2(x,y)\n",
    "print('方差未知且不同:',result2)\n",
    "\n",
    "#下面调用Scipy的t检验函数，分为方差相同和不同\n",
    "result3 = st.ttest_ind(x,y,equal_var=True)\n",
    "print('t检验，方差相同：',result3)\n",
    "result4 = st.ttest_ind(x,y,equal_var=False)\n",
    "print('t检验，方差不同：',result4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.4 两个正态总体的方差比$\\sigma^2_1$/$\\sigma^2_2$的区间估计\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "方差用来描述数据的分散程度\n",
    "'''\n",
    "VRateResultSet = namedtuple('VRateResultSet',['Rate','DF1','DF2','Lower','Upper'])\n",
    "def interval_var2(x,y,mu=np.array([float('Inf'),float('Inf')]),alpha=0.05):\n",
    "    n1 = len(x)#样本量\n",
    "    n2 = len(y)\n",
    "    \n",
    "    if np.all(mu < float('Inf')):#均值已知\n",
    "        #根据已知均值计算方差\n",
    "        Sx2 = 1/n1*np.sum((x-mu[0])**2)\n",
    "        Sy2 = 1/n2*np.sum((y-mu[1])**2)\n",
    "        df1 = n1\n",
    "        df2 = n2        \n",
    "    else:#均值未知\n",
    "        #直接计算样本方差作为总体方差的无偏估计，此时自由度等于：样本量-1\n",
    "        Sx2 = st.tvar(x)\n",
    "        Sy2 = st.tvar(y)\n",
    "        df1 = n1-1\n",
    "        df2 = n2-1\n",
    "    rate = Sx2/Sy2\n",
    "    lower = rate/st.f.ppf(1-alpha/2,df1,df2)\n",
    "    upper = rate/st.f.ppf(alpha/2,df1,df2)\n",
    "    result = VRateResultSet(Rate=rate,DF1=df1,DF2=df2,\n",
    "                            Lower=np.round(lower,6),\n",
    "                            Upper=np.round(upper,6))\n",
    "    return result"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 示例：\n",
    "![18.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A13_1.jpg](attachment:18.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A13_1.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均值已知的方差比： VRateResultSet(Rate=0.7326007326007866, DF1=13, DF2=8, Lower=0.176014, Upper=2.482042)\n",
      "均值未知的方差比： VRateResultSet(Rate=0.5837405184048123, DF1=12, DF2=7, Lower=0.12511, Upper=2.105269)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "示例：比较两组数据的方差，也就数据的波动情况\n",
    "'''\n",
    "x = np.array([79.98,80.04, 80.02, 80.04, 80.03, 80.03, 80.04, 79.97,\n",
    "              80.05, 80.03, 80.02, 80.00, 80.02])\n",
    "y = np.array([80.02, 79.94, 79.98, 79.97, 79.97, 80.03, 79.95, 79.97])\n",
    "#均值已知\n",
    "result1 = interval_var2(x,y,np.array([80,80]))\n",
    "print('均值已知的方差比：',result1)\n",
    "#均值未知\n",
    "result2 = interval_var2(x,y)\n",
    "print('均值未知的方差比：',result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用Scipy计算方差比置信区间(均值已知)： [0.176014 2.482042]\n",
      "使用Scipy计算方差比置信区间(均值未知)： [0.12511  2.105269]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "使用f分布的interval函数计算方差比的置信区间，分为已知均值和未知均值两种\n",
    "'''\n",
    "#注意：自由度设置顺序，第一个自由度为分母的自由度，第二个为分子的自由度。scale为x和y的方差之比\n",
    "result1 = st.f.interval(0.95,len(y),len(x),\n",
    "                        scale=(np.sum((x-80)**2)/len(x))/(np.sum((y-80)**2)/len(y)))\n",
    "print('使用Scipy计算方差比置信区间(均值已知)：',np.round(result1,6))\n",
    "\n",
    "#使用样本方差作为总体方差的无偏估计，则自由度分别减1。\n",
    "result2 = st.f.interval(0.95,len(y)-1,len(x)-1,scale=st.tvar(x)/st.tvar(y))\n",
    "print('使用Scipy计算方差比置信区间(均值未知)：',np.round(result2,6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.5 非正态分布总体均值的区间估计\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "利用中心极限定理，将无法肯定符合正态分布的数据转换成正态分布的区间估计问题。\n",
    "'''\n",
    "NNResultSet = namedtuple('NNResultSet',['Mean','Lower','Upper'])\n",
    "def interval_mu3(x,sigma=-1,alpha=0.05):\n",
    "    n = len(x)\n",
    "    mx = np.mean(x)\n",
    "    if sigma>0:\n",
    "        tmp = sigma/np.sqrt(n)*st.norm.ppf(1-alpha/2)\n",
    "    else:\n",
    "        tmp = st.tstd(x)/np.sqrt(n)*st.norm.ppf(1-alpha/2)\n",
    "    return NNResultSet(Mean = mx,Lower=np.round(mx-tmp,6),Upper=np.round(mx+tmp,6))"
   ]
  },
  {
   "attachments": {
    "19.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A12.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 示例：\n",
    "![19.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A12.jpg](attachment:19.%E5%8C%BA%E9%97%B4%E4%BC%B0%E8%AE%A12.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "方差未知的非正态总体均值区间估计： NNResultSet(Mean=2.243309669076189, Lower=2.105121, Upper=2.381498)\n",
      "调用interval函数估计非正态总体均值置信区间： [2.105121 2.381498]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "示例：将符合指数分布的电池寿命数据转换成正态分布的均值区间估计\n",
    "'''\n",
    "#生成样本数据。样本量越大，区间估计越精确\n",
    "x = st.expon.rvs(scale=2.266,size=1000)\n",
    "#方差未知\n",
    "result1 = interval_mu3(x)\n",
    "print('方差未知的非正态总体均值区间估计：',result1)\n",
    "#调用Scipy的interval函数估计区间，方差未知。\n",
    "result2 = st.norm.interval(0.95,loc = np.mean(x),scale=st.tstd(x)/(len(x)**0.5))\n",
    "print('调用interval函数估计非正态总体均值置信区间：',np.round(result2,6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.6 单侧置信区间估计\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "一个总体的均值单侧置信区间估计的用途也比较广泛，比如设备、元件等只关心最低寿命，即平均寿命的下限\n",
    "对产品的生产一般只关心最大废品率，也就是废品率的上限。\n",
    "'''\n",
    "UniMuResultSet = namedtuple('UniMuResultSet',['Mean','DF','Lower','Upper'])\n",
    "def interval_mu4(x, sigma=-1,side='two-sided',alpha=0.05):\n",
    "    n=len(x)\n",
    "    mx=np.mean(x)\n",
    "    if sigma>0:#已知总体方差\n",
    "        if side=='upper':#单侧置信上限\n",
    "            tmp=sigma/np.sqrt(n)*st.norm.ppf(1-alpha)\n",
    "            lower=float('-Inf')\n",
    "            upper=mx+tmp\n",
    "        elif side=='lower':#单侧置信下限\n",
    "            tmp=sigma/np.sqrt(n)*st.norm.ppf(1-alpha)\n",
    "            lower=mx-tmp\n",
    "            upper=float('Inf')\n",
    "        elif side=='two-sided':#双侧置信区间\n",
    "            #当进行双侧置信区间估计时，分位数的概率为1-alpha/2或alpha/2\n",
    "            tmp=sigma/np.sqrt(n)*st.norm.ppf(1-alpha/2)\n",
    "            lower=mx-tmp\n",
    "            upper=mx+tmp\n",
    "        df=n\n",
    "    else:#总体方差未知\n",
    "        if side=='upper':#置信上限\n",
    "            tmp=st.tstd(x)/np.sqrt(n)*st.t.ppf(1-alpha,n-1)\n",
    "            lower=float('-Inf')\n",
    "            upper=mx+tmp\n",
    "        elif side=='lower':#单侧置信下限\n",
    "            tmp=st.tstd(x)/np.sqrt(n)*st.t.ppf(1-alpha,n-1)\n",
    "            lower=mx-tmp\n",
    "            upper=float('Inf')\n",
    "        elif side=='two-sided':#双侧置信区间\n",
    "            #当进行双侧置信区间估计时，分位数的概率为1-alpha/2或alpha/2\n",
    "            tmp=st.tstd(x)/np.sqrt(n)*st.t.ppf(1-alpha/2,n-1)\n",
    "            lower=mx-tmp\n",
    "            upper=mx+tmp\n",
    "        df=n-1\n",
    "    return UniMuResultSet(Mean=mx,DF=df,Lower=np.round(lower,6),Upper=np.round(upper,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单侧置信下限： UniMuResultSet(Mean=997.1, DF=9, Lower=920.844338, Upper=inf)\n",
      "Scipy求单侧置信下限： 920.844338\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "示例：从一批零件中随机抽取10只做寿命测试，测得寿命（单位：小时）如下：\n",
    "    1067,919,1196,785,1126,936,918,1156,920,948\n",
    "零件寿命服从正态分布，求寿命平均值的置信度为0.95的单侧置信下限。\n",
    "'''\n",
    "#未知总体方差的情况下的单侧置信下限。\n",
    "x = np.array([1067,919,1196,785,1126,936,918,1156,920,948])\n",
    "#计算零件寿命的单侧置信下限，即95%的零件寿命大于多少\n",
    "result1 = interval_mu4(x,side='lower')\n",
    "print('单侧置信下限：',result1)\n",
    "\n",
    "#Scipy在求单侧置信区间的上限或下限时，其实就是求标准误在概率分布为5%时的百分位数\n",
    "result2 = st.t.ppf(0.05,df = len(x)-1,loc=np.mean(x),scale=st.tstd(x)/len(x)**0.5)\n",
    "print('Scipy求单侧置信下限：',np.round(result2,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单侧置信下限： UniMuResultSet(Mean=997.1, DF=9, Lower=-inf, Upper=1073.355662)\n",
      "Scipy求单侧置信上限： 1073.355662\n"
     ]
    }
   ],
   "source": [
    "#计算零件寿命的单侧置信上限，即95%的零件寿命小于多少\n",
    "result3 = interval_mu4(x,side='upper')\n",
    "print('单侧置信下限：',result3)\n",
    "\n",
    "#Scipy在求单侧置信区间的上限或下限时，就是求标准误在概率分布为95%时的百分位数\n",
    "result4 = st.t.ppf(0.95,df = len(x)-1,loc=np.mean(x),scale=st.tstd(x)/len(x)**0.5)\n",
    "print('Scipy求单侧置信上限：',np.round(result4,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ppf计算单侧置信区间的分解(单侧置信上限)： 1073.355662\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#上文中result4的计算公式可以分解如下：t分布百分数*标准误+均值，\n",
    "#其实就是前述公式图片和自定义函数中的计算公式\n",
    "result4_1 = st.t.ppf(0.95,df=len(x)-1)*st.tstd(x)/len(x)**0.5+np.mean(x)\n",
    "print('ppf计算单侧置信区间的分解(单侧置信上限)：',np.round(result4_1,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval函数计算双侧置信区间： [ 902.996528 1091.203472]\n",
      "ppf函数计算双侧置信区间的上限： 1091.203472\n",
      "ppf函数计算双侧置信区间的下限： 902.996528\n",
      "interval_mu4函数计算双侧置信区间： UniMuResultSet(Mean=997.1, DF=9, Lower=902.996528, Upper=1091.203472)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "双侧置信区间的计算以及分解\n",
    "'''\n",
    "#利用interval函数计算双侧置信区间，结果与interval_mu4一致（设side='two-sided'）\n",
    "result5_1 = st.t.interval(0.95,df=len(x)-1,loc=np.mean(x),scale=st.tstd(x)/len(x)**0.5)\n",
    "print('interval函数计算双侧置信区间：',np.round(result5_1,6))\n",
    "#ppf函数分别计算双侧置信区间的上限和下限。注意：和计算单侧置信区间的上下限是有区别的。\n",
    "result5_2 = st.t.ppf(0.975,df=len(x)-1,loc=np.mean(x),scale=st.tstd(x)/len(x)**0.5)\n",
    "print('ppf函数计算双侧置信区间的上限：',np.round(result5_2,6))\n",
    "result5_3 = st.t.ppf(0.025,df=len(x)-1,loc=np.mean(x),scale=st.tstd(x)/len(x)**0.5)\n",
    "print('ppf函数计算双侧置信区间的下限：',np.round(result5_3,6))\n",
    "result5_4 = interval_mu4(x,side='two-sided')\n",
    "print('interval_mu4函数计算双侧置信区间：',result5_4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (2)单总体方差$\\sigma^2$的单侧置信区间\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "单个总体的方差单侧置信区间用途也很广泛\n",
    "'''\n",
    "UniVarResultSet = namedtuple('UniVarResultSet',['Var','DF','Lower','Upper'])\n",
    "def interval_var3(x,mu=float('Inf'),side='two-sided',alpha=0.05):\n",
    "    n=len(x)\n",
    "    if mu<float('Inf'):\n",
    "        S2=np.sum((x-mu)**2)/n\n",
    "        df=n\n",
    "    else:\n",
    "        S2=st.tvar(x)\n",
    "        df=n-1\n",
    "    if side=='upper':#置信上限\n",
    "        lower=0\n",
    "        upper=df*S2/st.chi2.ppf(alpha,df)\n",
    "    elif side=='lower':#置信下限\n",
    "        lower=df*S2/st.chi2.ppf(1-alpha,df)\n",
    "        upper=float('Inf')\n",
    "    elif side=='two-sided':#双侧置信区间\n",
    "        lower=df*S2/st.chi2.ppf(1-alpha/2,df)\n",
    "        upper=df*S2/st.chi2.ppf(alpha/2,df)\n",
    "    \n",
    "    return UniVarResultSet(Var=np.round(S2,7),DF=df,\n",
    "                           Lower=np.round(lower,7),\n",
    "                           Upper=np.round(upper,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "自定义函数interval_var3求解置信上限： UniVarResultSet(Var=0.0583333, DF=9, Lower=0, Upper=0.1578894)\n",
      "自定义函数interval_var3求解置信下限： UniVarResultSet(Var=0.0583333, DF=9, Lower=0.0310302, Upper=inf)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "计算前述零件重量的例子，比较关心重量的最大误差是多少\n",
    "\n",
    "示例：抽样某零件样品的重量共10次，(单位：kg)，分别为：\n",
    "    10.1,10,9.8,10.5,9.7,10.1,9.9,10.2,10.3,9.9\n",
    "    求零件重量方差的单侧置信区间上限，即其最大误差是多少。\n",
    "'''\n",
    "x=np.array([10.1,10,9.8,10.5,9.7,10.1,9.9,10.2,10.3,9.9])\n",
    "print('自定义函数interval_var3求解置信上限：',interval_var3(x,side='upper'))\n",
    "print('自定义函数interval_var3求解置信下限：',interval_var3(x,side='lower'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 通过ppf函数求解单侧置信区间\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.325112843066815, 33.251128430668146, 6.325112843066815, 36.251128430668146)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###ppf函数的loc和scale两个参数的涵义和用法\n",
    "st.chi2.ppf(0.05,9),st.chi2.ppf(0.05,9,scale=10),\\\n",
    "st.chi2.ppf(0.05,9,loc=3),st.chi2.ppf(0.05,9,loc=3,scale=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.251128430668146"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###ppf(p, ..., loc, scale)=ppf(p, ...)*scale+loc\n",
    "st.chi2.ppf(0.05,9)*10+3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### $\\mu$未知的总体方差单侧置信下限、上限\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ppf函数求置信上限： 0.157889\n",
      "ppf函数求置信下限： 0.03103\n"
     ]
    }
   ],
   "source": [
    "#ppf函数求置信上限\n",
    "result1 = 1/st.chi2.ppf(0.05,df=len(x)-1,scale=1/((len(x)-1)*st.tvar(x)))\n",
    "print('ppf函数求置信上限：',np.round(result1,6))\n",
    "#ppf函数计算置信下限\n",
    "result2 = 1/st.chi2.ppf(0.95,df=len(x)-1,scale=1/((len(x)-1)*st.tvar(x)))\n",
    "print('ppf函数求置信下限：',np.round(result2,6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (3) 两个总体求均值之差$\\mu_1$-$\\mu_2$单侧置信区间上、下限\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "两个总体求均值之差的单侧置信区间，如果已知总体的方差，则是使用总体方差。\n",
    "如果未知，则使用样本方差的无偏估计代替。\n",
    "'''\n",
    "DsMuResultSet = namedtuple('DsMuResultSet',['Mean','DF','Lower','Upper'])\n",
    "def interval_mu5(x,y, sigma=np.array([-1,-1]),vareq=False,side='two-sided',alpha=0.05):\n",
    "    n1=len(x)\n",
    "    n2=len(y)\n",
    "    mx=np.mean(x)\n",
    "    my=np.mean(y)\n",
    "    diff=mx-my\n",
    "    if np.all(sigma>=0):\n",
    "        if side=='upper':#置信上限\n",
    "            tmp=st.norm.ppf(1-alpha)*np.sqrt(sigma[0]**2/n1+sigma[1]**2/n2)\n",
    "            lower=float('-Inf')\n",
    "            upper=diff+tmp\n",
    "        elif side=='lower':#置信下限\n",
    "            tmp=st.norm.ppf(1-alpha)*np.sqrt(sigma[0]**2/n1+simga[1]**2/n2)\n",
    "            lower=diff-tmp\n",
    "            upper=float('Inf')\n",
    "        elif side=='two-sided':#双侧置信区间\n",
    "            tmp=st.norm.ppf(1-alpha/2)*np.sqrt(sigma[0]**2/n1+sigma[1]**2/n2)\n",
    "            lower=diff-tmp\n",
    "            upper=diff+tmp\n",
    "        df=n1+n2\n",
    "    else:\n",
    "        if vareq:\n",
    "            sw=((n1-1)*st.tvar(x)+(n2-1)*st.tvar(y))/(n1+n2-2)\n",
    "            if side=='upper':\n",
    "                tmp=np.sqrt(sw*(1/n1+1/n2))*st.t.ppf(1-alpha,n1+n2-2)\n",
    "                lower=float('-Inf')\n",
    "                upper=diff+tmp\n",
    "            elif side=='lower':\n",
    "                tmp=np.sqrt(sw*(1/n1+1/n2))*st.t.ppf(1-alpha,n1+n2-2)\n",
    "                lower=diff-tmp\n",
    "                upper=float('Inf')\n",
    "            elif side=='two-sided':\n",
    "                tmp=np.sqrt(sw*(1/n1+1/n2))*st.t.ppf(1-alpha/2,n1+n2-2)\n",
    "                lower=diff-tmp\n",
    "                upper=diff+tmp\n",
    "            df=n1+n2-2\n",
    "        else:\n",
    "            s1=st.tvar(x)\n",
    "            s2=st.tvar(y)\n",
    "            nu=(s1/n1+s2/n2)**2/(s1**2/n1**2/(n1-1)+s2**2/n2**2/(n2-1))\n",
    "            if side=='upper':\n",
    "                tmp=st.t.ppf(1-alpha,nu)*np.sqrt(s1/n1+s2/n2)\n",
    "                lower=float('-Inf')\n",
    "                upper=diff+tmp\n",
    "            elif side=='lower':\n",
    "                tmp=st.t.ppf(1-alpha,nu)*np.sqrt(s1/n1+s2/n2)\n",
    "                lower=diff-tmp\n",
    "                upper=float('Inf')\n",
    "            elif side=='two-sided':\n",
    "                tmp=st.t.ppf(1-alpha/2,nu)*np.sqrt(s1/n1+s2/n2)\n",
    "                lower=diff-tmp\n",
    "                upper=diff+tmp\n",
    "            df=nu\n",
    "    return DsMuResultSet(Mean=np.round(diff,6),\n",
    "                         DF=np.round(df,6),\n",
    "                         Lower=np.round(lower,6),\n",
    "                         Upper=np.round(upper,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DsMuResultSet(Mean=1.100398, DF=26.537474, Lower=-inf, Upper=3.249745),\n",
       " DsMuResultSet(Mean=1.100398, DF=26.537474, Lower=-1.048948, Upper=inf),\n",
       " DsMuResultSet(Mean=1.100398, DF=26.537474, Lower=-1.489276, Upper=3.690073))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "应用前述灌装水例子\n",
    "'''\n",
    "x = st.norm.rvs(501.1,2.4,12)\n",
    "y = st.norm.rvs(499.7,4.7,17)\n",
    "#单侧置信区间上限、下限以及双侧置信区间的计算结果\n",
    "interval_mu5(x,y,side='upper'),interval_mu5(x,y,side='lower'),\\\n",
    "interval_mu5(x,y,side='two-sided')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (4)两个总体方差比$\\sigma^2_1$ /$\\sigma^2_2$ 的单侧置信区间估计\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "比较两个样本数据测量的误差大小，特别是方差比的单侧置信下限、上限\n",
    "'''\n",
    "DsVRateResultSet = namedtuple('DsVRateResultSet',['Rate','DF1','DF2','Lower','Upper'])\n",
    "def interval_var4(x,y,mu=np.array([float('Inf'),float('Inf')]),side='two-sided',alpha=0.05):\n",
    "    n1=len(x)\n",
    "    n2=len(y)\n",
    "    if np.all(mu<float('Inf')):\n",
    "        sx2=1/n1*np.sum((x-mu[0])**2)\n",
    "        df1=n1\n",
    "        sy2=1/n2*np.sum((y-mu[1])**2)\n",
    "        df2=n2\n",
    "    else:\n",
    "        sx2=st.tvar(x)\n",
    "        sy2=st.tvar(y)\n",
    "        df1=n1-1\n",
    "        df2=n2-1\n",
    "    rate=sx2/sy2\n",
    "    if side=='upper':\n",
    "        lower=0\n",
    "        upper=rate/st.f.ppf(alpha,df1,df2)\n",
    "    elif side=='lower':\n",
    "        lower=rate/st.f.ppf(1-alpha,df1,df2)\n",
    "        upper=float('Inf')\n",
    "    elif side=='two-sided':\n",
    "        lower=rate/st.f.ppf(1-alpha/2,df1,df2)\n",
    "        upper=rate/st.f.ppf(alpha/2,df1,df2)\n",
    "    \n",
    "    return DsVRateResultSet(Rate=np.round(rate,6),\n",
    "                            DF1=df1,DF2=df2,\n",
    "                            Lower=np.round(lower,6),\n",
    "                            Upper=np.round(upper,6))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DsVRateResultSet(Rate=0.583741, DF1=12, DF2=7, Lower=0, Upper=1.700645),\n",
       " DsVRateResultSet(Rate=0.583741, DF1=12, DF2=7, Lower=0.163299, Upper=inf),\n",
       " DsVRateResultSet(Rate=0.583741, DF1=12, DF2=7, Lower=0.12511, Upper=2.105269))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "仍用上文中方差比双侧置信区间的例子。\n",
    "'''\n",
    "x = np.array([79.98,80.04, 80.02, 80.04, 80.03, 80.03, 80.04, 79.97,\n",
    "              80.05, 80.03, 80.02, 80.00, 80.02])\n",
    "y = np.array([80.02, 79.94, 79.98, 79.97, 79.97, 80.03, 79.95, 79.97])\n",
    "#单侧置信区间的上限、下限，双侧置信区间\n",
    "interval_var4(x,y,side='upper'),interval_var4(x,y,side='lower'), \\\n",
    "interval_var4(x,y,side='two-sided')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单侧置信上限： 1.7006452137148957\n",
      "单侧置信下限： 0.16329884036224449\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "ppf函数计算单侧置信区间\n",
    "'''\n",
    "#置信上限\n",
    "print('单侧置信上限：',1/st.f.ppf(0.05,len(x)-1,len(y)-1,scale=st.tvar(y)/st.tvar(x)))\n",
    "#置信下限\n",
    "print('单侧置信下限：',1/st.f.ppf(0.95,len(x)-1,len(y)-1,scale=st.tvar(y)/st.tvar(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  }
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